A more insightful approach would be to transform the point cloud into a proper summary, i.e., a representation, equally informative and easily readable. Pertinently, hierarchical clustering dendrograms have been extensively studied in the last decades as they unveil the intrinsic relationship among points of a point cloud (for a review on hierarchical clustering dendrograms see [1]}).
In our setting, the rationale behind hierarchical clustering stems from the need to quantify to which extent lesions, i.e., their radiomic vectors, are similar within patients and how they get agglomerated, hierarchically, one to each other.
A dendrogram is obtained in such a way that lesions are linked in terms of evolutionary relationship, based on similarities in their imaging characteristics. Fig. REF  graphically describes the process while Appendix  formalizes the mathematical steps involved.
Dendrograms' structure reflects the homogeneity between points of the point cloud. For instance, Fig. REF  presents three dendrograms: the blue one describes a condensed point cloud, the green one presents a scattered point cloud while the orange tree denotes a hybrid situation.
